{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:50:59Z","timestamp":1773514259014,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T00:00:00Z","timestamp":1527724800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671126"],"award-info":[{"award-number":["61671126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["13\/SIRG\/2178"],"award-info":[{"award-number":["13\/SIRG\/2178"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s00034-018-0859-8","type":"journal-article","created":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T10:51:33Z","timestamp":1527763893000},"page":"304-328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Image Block Compressive Sensing Reconstruction via Group-Based Sparse Representation and Nonlocal Total Variation"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6021-5551","authenticated-orcid":false,"given":"Jin","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yuansong","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Zhizhong","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Quan","family":"Wen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,31]]},"reference":[{"issue":"4","key":"859_CR1","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/MSP.2007.4286571","volume":"24","author":"RG Baraniuk","year":"2007","unstructured":"R.G. Baraniuk, Compressive sensing. IEEE Signal Process. Mag. 24(4), 118\u2013120+124 (2007)","journal-title":"IEEE Signal Process. Mag."},{"issue":"1","key":"859_CR2","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1137\/060657704","volume":"51","author":"AM Bruckstein","year":"2009","unstructured":"A.M. Bruckstein, D.L. Donoho, M. Elad, From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51(1), 34\u201381 (2009)","journal-title":"SIAM Rev."},{"issue":"2","key":"859_CR3","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1137\/040616024","volume":"4","author":"A Buades","year":"2005","unstructured":"A. Buades, B. Coll, J. Morel, A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490\u2013530 (2005)","journal-title":"Multiscale Model. Simul."},{"key":"859_CR4","unstructured":"C. Chen, E.W. Tramel, J.E. Fowler, Compressed-sensing recovery of images and video using multihypothesis predictions, in Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States (2011), pp. 1193\u20131198"},{"key":"859_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.jvcir.2016.03.018","volume":"38","author":"G Chen","year":"2016","unstructured":"G. Chen, J. Zhang, D. Li, Fractional-order total variation combined with sparsifying transforms for compressive sensing sparse image reconstruction. J. Vis. Commun. Image Represent. 38, 407\u2013422 (2016)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"4","key":"859_CR6","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1007\/s00034-016-0432-2","volume":"36","author":"J Chen","year":"2017","unstructured":"J. Chen, Y. Gao, C. Ma, Y. Kuo, Compressive sensing image reconstruction based on multiple regulation constraints. Circuits Syst. Signal Process. 36(4), 1621\u20131638 (2017)","journal-title":"Circuits Syst. Signal Process."},{"issue":"8","key":"859_CR7","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1109\/TIP.2014.2329449","volume":"23","author":"W Dong","year":"2014","unstructured":"W. Dong, G. Shi, X. Li, Y. Ma, F. Huang, Compressive sensing via nonlocal low-rank regularization. IEEE Trans. Image Process. 23(8), 3618\u20133632 (2014)","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"859_CR8","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1016\/j.image.2012.09.003","volume":"27","author":"W Dong","year":"2012","unstructured":"W. Dong, G. Shi, X. Li, L. Zhang, X. Wu, Image reconstruction with locally adaptive sparsity and nonlocal robust regularization. Signal Process. Image Commun. 27(10), 1109\u20131122 (2012)","journal-title":"Signal Process. Image Commun."},{"issue":"3","key":"859_CR9","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/18.382009","volume":"41","author":"DL Donoho","year":"1995","unstructured":"D.L. Donoho, De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613\u2013627 (1995)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"4","key":"859_CR10","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289\u20131306 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"7","key":"859_CR11","doi-asserted-by":"publisher","first-page":"3126","DOI":"10.1109\/TIP.2016.2562563","volume":"25","author":"N Eslahi","year":"2016","unstructured":"N. Eslahi, A. Aghagolzadeh, Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization. IEEE Trans. Image Process. 25(7), 3126\u20133140 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"859_CR12","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/LSP.2013.2280571","volume":"20","author":"X Fei","year":"2013","unstructured":"X. Fei, Z. Wei, L. Xiao, Iterative directional total variation refinement for compressive sensing image reconstruction. IEEE Signal Process. Lett. 20(11), 1070\u20131073 (2013)","journal-title":"IEEE Signal Process. Lett."},{"key":"859_CR13","unstructured":"L. Gan, Block compressed sensing of natural images, in: 15th International Conference on Digital Signal Processing, Wales, United Kingdom (2007), pp. 403\u2013406"},{"issue":"7","key":"859_CR14","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1016\/j.sigpro.2012.12.008","volume":"93","author":"A Gholami","year":"2013","unstructured":"A. Gholami, S.M. Hosseini, A balanced combination of Tikhonov and total variation regularizations for reconstruction of piecewise-smooth signals. Signal Process. 93(7), 1945\u20131960 (2013)","journal-title":"Signal Process."},{"issue":"3","key":"859_CR15","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1137\/070698592","volume":"7","author":"G Gilboa","year":"2008","unstructured":"G. Gilboa, S. Osher, Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7(3), 1005\u20131028 (2008)","journal-title":"Multiscale Model. Simul."},{"issue":"2","key":"859_CR16","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/080725891","volume":"2","author":"T Goldstein","year":"2009","unstructured":"T. Goldstein, S. Osher, The split Bregman method for l1-regularized problems. SIAM J. Imaging Sci. 2(2), 323\u2013343 (2009)","journal-title":"SIAM J. Imaging Sci."},{"issue":"9","key":"859_CR17","doi-asserted-by":"publisher","first-page":"3488","DOI":"10.1109\/TSP.2009.2022003","volume":"57","author":"L He","year":"2009","unstructured":"L. He, L. Carin, Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans. Signal Process. 57(9), 3488\u20133497 (2009)","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"859_CR18","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/TIP.2010.2092433","volume":"20","author":"M Jung","year":"2011","unstructured":"M. Jung, X. Bresson, T.F. Chan, L.A. Vese, Nonlocal Mumford-Shah regularizers for color image restoration. IEEE Trans. Image Process. 20(6), 1583\u20131598 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"859_CR19","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s10589-013-9576-1","volume":"56","author":"C Li","year":"2013","unstructured":"C. Li, W. Yin, H. Jiang, Y. Zhang, An efficient augmented Lagrangian method with applications to total variation minimization. Comput. Optim. Appl. 56(3), 507\u2013530 (2013)","journal-title":"Comput. Optim. Appl."},{"key":"859_CR20","unstructured":"S. Mun, J.E. Fowler, Block compressed sensing of images using directional transforms, in IEEE International Conference on Image Processing, Cairo, Egypt (2009), pp. 3021\u20133024"},{"issue":"1\u20134","key":"859_CR21","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"L Rudin","year":"1992","unstructured":"L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1\u20134), 259\u2013259 (1992)","journal-title":"Phys. D Nonlinear Phenom."},{"issue":"P3","key":"859_CR22","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1016\/j.neucom.2014.06.082","volume":"151","author":"Y Shen","year":"2015","unstructured":"Y. Shen, J. Li, Z. Zhu, W. Cao, Y. Song, Image reconstruction algorithm from compressed sensing measurements by dictionary learning. Neurocomputing 151(P3), 1153\u20131162 (2015)","journal-title":"Neurocomputing"},{"key":"859_CR23","first-page":"1035","volume":"4","author":"A Tikhonov","year":"1963","unstructured":"A. Tikhonov, Solution of incorrectly formulated problems and the regularization method. Sov. Meth. Dokl. 4, 1035\u20131038 (1963)","journal-title":"Sov. Meth. Dokl."},{"key":"859_CR24","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neucom.2016.10.051","volume":"224","author":"Q Wang","year":"2017","unstructured":"Q. Wang, D. Li, Y. Shen, Intelligent nonconvex compressive sensing using prior information for image reconstruction by sparse representation. Neurocomputing 224, 71\u201381 (2017)","journal-title":"Neurocomputing"},{"issue":"2","key":"859_CR25","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/TIP.2011.2163520","volume":"21","author":"X Wu","year":"2012","unstructured":"X. Wu, W. Dong, X. Zhang, G. Shi, Model-assisted adaptive recovery of compressed sensing with imaging applications. IEEE Trans. Image Process. 21(2), 451\u2013458 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"859_CR26","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1016\/j.sigpro.2012.04.001","volume":"92","author":"J Xu","year":"2012","unstructured":"J. Xu, J. Ma, D. Zhang, Y. Zhang, S. Lin, Improved total variation minimization method for compressive sensing by intra-prediction. Signal Process. 92(11), 2614\u20132623 (2012)","journal-title":"Signal Process."},{"key":"859_CR27","unstructured":"J. Zhang, S. Liu, R. Xiong, S. Ma, D. Zhao, Improved total variation based image compressive sensing recovery by nonlocal regularization, in IEEE International Symposium on Circuits and Systems, Beijing, China (2013), pp. 2836\u20132839"},{"key":"859_CR28","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.sigpro.2013.09.025","volume":"103","author":"J Zhang","year":"2014","unstructured":"J. Zhang, C. Zhao, D. Zhao, W. Gao, Image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization. Signal Process. 103, 114\u2013126 (2014)","journal-title":"Signal Process."},{"issue":"8","key":"859_CR29","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1109\/TIP.2014.2323127","volume":"23","author":"J Zhang","year":"2014","unstructured":"J. Zhang, D. Zhao, W. Gao, Group-based sparse representation for image restoration. IEEE Trans. Image Process. 23(8), 3336\u20133351 (2014)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"859_CR30","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1109\/TCSVT.2014.2302380","volume":"24","author":"J Zhang","year":"2014","unstructured":"J. Zhang, D. Zhao, R. Xiong, S. Ma, W. Gao, Image restoration using joint statistical modeling in a space-transform domain. IEEE Trans. Circuits Syst. Video Technol. 24(6), 915\u2013928 (2014)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"3","key":"859_CR31","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/JETCAS.2012.2220391","volume":"2","author":"J Zhang","year":"2012","unstructured":"J. Zhang, D. Zhao, C. Zhao, R. Xiong, S. Ma, W. Gao, Image compressive sensing recovery via collaborative sparsity. IEEE J. Emerg. Sel. Top. Circuits Syst. 2(3), 380\u2013391 (2012)","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"859_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.sigpro.2015.11.025","volume":"123","author":"K Zhang","year":"2016","unstructured":"K. Zhang, X. Gao, J. Li, H. Xia, Single image super-resolution using regularization of non-local steering kernel regression. Signal Process. 123, 53\u201363 (2016)","journal-title":"Signal Process."},{"issue":"3","key":"859_CR33","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1137\/090746379","volume":"3","author":"X Zhang","year":"2010","unstructured":"X. Zhang, M. Burger, X. Bresson, S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3(3), 253\u2013276 (2010)","journal-title":"SIAM J. Imaging Sci."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00034-018-0859-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-018-0859-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-018-0859-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T21:33:44Z","timestamp":1559252024000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00034-018-0859-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,31]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["859"],"URL":"https:\/\/doi.org\/10.1007\/s00034-018-0859-8","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,31]]},"assertion":[{"value":"16 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}